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arXiv · 2602.22228

Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals

Jiyeong Kim·Stephen P. Ma·Nirali Vora·Nicholas W. Larsen·Julia Adler-Milstein·Jonathan H. Chen·Selen Bozkurt·Abeed Sarker·Juhee Cho·Jindeok Joo·Natali Pageler·Fatima Rodriguez·Christopher Sharp·Eleni Linos
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Abstract

Stroke affected millions annually, yet poor symptom recognition often delayed care-seeking. To address risk recognition gap, we developed a passive surveillance system for early stroke risk detection using patient-reported symptoms among individuals with diabetes. Constructing a symptom taxonomy grounded in patients own language and a dual machine learning pipeline (heterogeneous GNN and EN/LASSO), we identified symptom patterns associated with subsequent stroke. We translated findings into a hybrid risk screening system integrating symptom relevance and temporal proximity, evaluated across 3-90 day windows through EHR-based simulations. Under conservative thresholds, intentionally designed to minimize false alerts, the screening system achieved high specificity (1.00) and prevalence-adjusted positive predictive value (1.00), with good sensitivity (0.72), an expected trade-off prioritizing precision, that was highest in 90-day window. Patient-reported language alone supported high-precision, low-burden early stroke risk detection, that could offer a valuable time window for clinical evaluation and intervention for high-risk individuals.

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Jiyeong Kim, Stephen P. Ma, Nirali Vora, Nicholas W. Larsen, Julia Adler-Milstein, Jonathan H. Chen, Selen Bozkurt, Abeed Sarker, Juhee Cho, Jindeok Joo, Natali Pageler, Fatima Rodriguez, Christopher Sharp, Eleni Linos. 2026-02-07. Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals. https://arxiv.org/abs/2602.22228

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